OpenRaiser/Intern-Atlas huggingface dataset:
Co-matching: Combating Noisy Labels by Augmentation Anchoring
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning with noisy labels is challenging as deep neural networks have the high capacity to memorize the noisy labels. In this paper, we propose a learning algorithm called Co-matching, which balances the consistency and divergence between two networks by augmentation anchoring. Specifically, we have one network generate anchoring label from its prediction on a weakly-augmented image. Meanwhile, we force its peer network, taking the strongly-augmented version of the same image as input, to generate prediction close to the anchoring label. We then update two networks simultaneously by selecting small-loss instances to minimize both unsupervised matching loss (i.e., measure the consistency of the two networks) and supervised classification loss (i.e. measure the classification performance). Besides, the unsupervised matching loss makes our method not heavily rely on noisy labels, which prevents memorization of noisy labels. Experiments on three benchmark datasets demonstrate that Co-matching achieves results comparable to the state-of-the-art methods.
A Survey of Distributed Optimization Methods for Multi-Robot Systems
Venue: arXiv cs.RO 2021
Year: 2021

Distributed optimization consists of multiple computation nodes working together to minimize a common objective function through local computation iterations and network-constrained communication steps. In the context of robotics, distributed optimization algorithms can enable multi-robot systems to accomplish tasks in the absence of centralized coordination. We present a general framework for applying distributed optimization as a module in a robotics pipeline. We survey several classes of distributed optimization algorithms and assess their practical suitability for multi-robot applications. We further compare the performance of different classes of algorithms in simulations for three prototypical multi-robot problem scenarios. The Consensus Alternating Direction Method of Multipliers (C-ADMM) emerges as a particularly attractive and versatile distributed optimization method for multi-robot systems.
Scam Pandemic: How Attackers Exploit Public Fear through Phishing
Venue: arXiv cs.CR 2021
Year: 2021

As the COVID-19 pandemic started triggering widespread lockdowns across the globe, cybercriminals did not hesitate to take advantage of users' increased usage of the Internet and their reliance on it. In this paper, we carry out a comprehensive measurement study of online social engineering attacks in the early months of the pandemic. By collecting, synthesizing, and analyzing DNS records, TLS certificates, phishing URLs, phishing website source code, phishing emails, web traffic to phishing websites, news articles, and government announcements, we track trends of phishing activity between January and May 2020 and seek to understand the key implications of the underlying trends. We find that phishing attack traffic in March and April 2020 skyrocketed up to 220\% of its pre-COVID-19 rate, far exceeding typical seasonal spikes. Attackers exploited victims' uncertainty and fear related to the pandemic through a variety of highly targeted scams, including emerging scam types against which current defenses are not sufficient as well as traditional phishing which outpaced the ecosystem's collective response.
A Plenum-Based Calibration Device for Tactile Sensor Arrays
Venue: arXiv cs.RO 2021
Year: 2021

In modern robotic applications, tactile sensor arrays (i.e., artificial skins) are an emergent solution to determine the locations of contacts between a robot and an external agent. Localizing the point of contact is useful but determining the force applied on the skin provides many additional possibilities. This additional feature usually requires time-consuming calibration procedures to relate the sensor readings to the applied forces. This letter presents a novel device that enables the calibration of tactile sensor arrays in a fast and simple way. The key idea is to design a plenum chamber where the skin is inserted, and then the calibration of the tactile sensors is achieved by relating the air pressure and the sensor readings. This general concept is tested experimentally to calibrate the skin of the iCub robot. The validation of the calibration device is achieved by placing the masses of known weight on the artificial skin and comparing the applied force against the one estimated by the sensors.
SETGAN: Scale and Energy Trade-off GANs for Image Applications on Mobile Platforms
Venue: arXiv cs.CV 2021
Year: 2021

We consider the task of photo-realistic unconditional image generation (generate high quality, diverse samples that carry the same visual content as the image) on mobile platforms using Generative Adversarial Networks (GANs). In this paper, we propose a novel approach to trade-off image generation accuracy of a GAN for the energy consumed (compute) at run-time called Scale-Energy Tradeoff GAN (SETGAN). GANs usually take a long time to train and consume a huge memory hence making it difficult to run on edge devices. The key idea behind SETGAN for an image generation task is for a given input image, we train a GAN on a remote server and use the trained model on edge devices. We use SinGAN, a single image unconditional generative model, that contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. During the training process, we determine the optimal number of scales for a given input image and the energy constraint from the target edge device. Results show that with SETGAN's unique client-server-based architecture, we were able to achieve a 56% gain in energy for a loss of 3% to 12% SSIM accuracy. Also, with the parallel multi-scale training, we obtain around 4x gain in training time on the server.
Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas
Venue: arXiv cs.CV 2021
Year: 2021

HDR reconstruction is an important task in computer vision with many industrial needs. The traditional approaches merge multiple exposure shots to generate HDRs that correspond to the physical quantity of illuminance of the scene. However, the tedious capturing process makes such multi-shot approaches inconvenient in practice. In contrast, recent single-shot methods predict a visually appealing HDR from a single LDR image through deep learning. But it is not clear whether the previously mentioned physical properties would still hold, without training the network to explicitly model them. In this paper, we introduce the physical illuminance constraints to our single-shot HDR reconstruction framework, with a focus on spherical panoramas. By the proposed physical regularization, our method can generate HDRs which are not only visually appealing but also physically plausible. For evaluation, we collect a large dataset of LDR and HDR images with ground truth illuminance measures. Extensive experiments show that our HDR images not only maintain high visual quality but also top all baseline methods in illuminance prediction accuracy.
Goal Seeking Quadratic Unconstrained Binary Optimization
Venue: arXiv cs.AI 2021
Year: 2021

The Quadratic Unconstrained Binary Optimization (QUBO) modeling and solution framework is a requirement for quantum and digital annealers. However optimality for QUBO problems of any practical size is extremely difficult to achieve. In order to incorporate the problem-specific insights, a diverse set of solutions meeting an acceptable target metric or goal is the preference in high level decision making. In this paper, we present two alternatives for goal-seeking QUBO for minimizing the deviation from a given target as well as a range of values around a target. Experimental results illustrate the efficacy of the proposed approach over Constraint Programming for quickly finding a satisficing set of solutions.
Detecting User-Perceived Failure in Mobile Applications via Mining User Traces
Venue: arXiv cs.SE 2021
Year: 2021

Mobile applications (apps) often suffer from failure nowadays. Developers usually pay more attention to the failure that is perceived by users and compromises the user experience. Existing approaches focus on mining large volume logs to detect failure, however, to our best knowledge, there is no approach focusing on detecting whether users have actually perceived failure, which directly influence the user experience. In this paper, we propose a novel approach to detecting user-perceived failure in mobile apps. By leveraging the frontend user traces, our approach first builds an app page model, and applies an unsupervised detection algorithm to detect whether a user has perceived failure. Our insight behind the algorithm is that when user-perceived failure occurs on an app page, the users will backtrack and revisit the certain page to retry. Preliminary evaluation results show that our approach can achieve good detection performance on a dataset collected from real world users.
Convex Online Video Frame Subset Selection using Multiple Criteria for Data Efficient Autonomous Driving
Venue: arXiv cs.LG 2021
Year: 2021

Training vision-based Urban Autonomous driving models is a challenging problem, which is highly researched in recent times. Training such models is a data-intensive task requiring the storage and processing of vast volumes of (possibly redundant) driving video data. In this paper, we study the problem of developing data-efficient autonomous driving systems. In this context, we study the problem of multi-criteria online video frame subset selection. We study convex optimization-based solutions and show that they are unable to provide solutions with high weightage to the loss of selected video frames. We design a novel convex optimization-based multi-criteria online subset selection algorithm that uses a thresholded concave function of selection variables. We also propose and study a submodular optimization-based algorithm. Extensive experiments using the driving simulator CARLA show that we are able to drop 80% of the frames while succeeding to complete 100% of the episodes w.r.t. the model trained on 100% data, in the most difficult task of taking turns. This results in a training time of less than 30% compared to training on the whole dataset. We also perform detailed experiments on prediction performances of various affordances used by the Conditional Affordance Learning (CAL) model and show that our subset selection improves performance on the crucial affordance "Relative Angle" during turns.
Knowledge-aware Contrastive Molecular Graph Learning
Venue: arXiv cs.LG 2021
Year: 2021

Leveraging domain knowledge including fingerprints and functional groups in molecular representation learning is crucial for chemical property prediction and drug discovery. When modeling the relation between graph structure and molecular properties implicitly, existing works can hardly capture structural or property changes and complex structure, with much smaller atom vocabulary and highly frequent atoms. In this paper, we propose the Contrastive Knowledge-aware GNN (CKGNN) for self-supervised molecular representation learning to fuse domain knowledge into molecular graph representation. We explicitly encode domain knowledge via knowledge-aware molecular encoder under the contrastive learning framework, ensuring that the generated molecular embeddings equipped with chemical domain knowledge to distinguish molecules with similar chemical formula but dissimilar functions. Extensive experiments on 8 public datasets demonstrate the effectiveness of our model with a 6\% absolute improvement on average against strong competitors. Ablation study and further investigation also verify the best of both worlds: incorporation of chemical domain knowledge into self-supervised learning.
The Value of Communication and Cooperation in a Two-Server Service System
Venue: arXiv cs.GT 2021
Year: 2021

In 2015, Guglielmi and Badia discussed optimal strategies in a particular type of service system with two strategic servers. In their setup, each server can either be active or inactive and an active server can be requested to transmit a sequence of packets. The servers have varying probabilities of successfully transmitting when they are active, and both servers receive a unit reward if the sequence of packets is transmitted successfully. Guglielmi and Badia provided an analysis of optimal strategies in four scenarios: where each server does not know the other's successful transmission probability; one of the two servers is always inactive; each server knows the other's successful transmission probability; and they are willing to cooperate. Unfortunately the analysis in Guglielmi and Badia contained errors. In this paper we correct these errors. We discuss three cases where both servers (I) communicate and cooperate; (II) neither communicate nor cooperate; (III) communicate but do not cooperate. In particular, we obtain the unique Nash equilibrium strategy in Case II through a Bayesian game formulation, and demonstrate that there is a region in the parameter space where there are multiple Nash equilibria in Case III. We also quantify the value of communication or cooperation by comparing the social welfare in the three cases, and propose possible regulations to make the Nash equilibrium strategy the socially optimal strategy for both Cases II and III.
A Fine-Grained Dataset and its Efficient Semantic Segmentation for Unstructured Driving Scenarios
Venue: arXiv cs.CV 2021
Year: 2021

Research in autonomous driving for unstructured environments suffers from a lack of semantically labeled datasets compared to its urban counterpart. Urban and unstructured outdoor environments are challenging due to the varying lighting and weather conditions during a day and across seasons. In this paper, we introduce TAS500, a novel semantic segmentation dataset for autonomous driving in unstructured environments. TAS500 offers fine-grained vegetation and terrain classes to learn drivable surfaces and natural obstacles in outdoor scenes effectively. We evaluate the performance of modern semantic segmentation models with an additional focus on their efficiency. Our experiments demonstrate the advantages of fine-grained semantic classes to improve the overall prediction accuracy, especially along the class boundaries. The dataset and pretrained model are available at mucar3.de/icpr2020-tas500.
MIcro-Surgical Anastomose Workflow recognition challenge report
Venue: arXiv cs.LG 2021
Year: 2021

The "MIcro-Surgical Anastomose Workflow recognition on training sessions" (MISAW) challenge provided a data set of 27 sequences of micro-surgical anastomosis on artificial blood vessels. This data set was composed of videos, kinematics, and workflow annotations described at three different granularity levels: phase, step, and activity. The participants were given the option to use kinematic data and videos to develop workflow recognition models. Four tasks were proposed to the participants: three of them were related to the recognition of surgical workflow at three different granularity levels, while the last one addressed the recognition of all granularity levels in the same model. One ranking was made for each task. We used the average application-dependent balanced accuracy (AD-Accuracy) as the evaluation metric. This takes unbalanced classes into account and it is more clinically relevant than a frame-by-frame score. Six teams, including a non-competing team, participated in at least one task. All models employed deep learning models, such as CNN or RNN. The best models achieved more than 95% AD-Accuracy for phase recognition, 80% for step recognition, 60% for activity recognition, and 75% for all granularity levels. For high levels of granularity (i.e., phases and steps), the best models had a recognition rate that may be sufficient for applications such as prediction of remaining surgical time or resource management. However, for activities, the recognition rate was still low for applications that can be employed clinically. The MISAW data set is publicly available to encourage further research in surgical workflow recognition. It can be found at www.synapse.org/MISAW
Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks with Wireless Power Transfer: A Distributed ADMM Approach
Venue: arXiv cs.IT 2021
Year: 2021

In multicell massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks, base stations (BSs) with multiple antennas deliver their radio frequency energy in the downlink, and Internet-of-Things (IoT) devices use their harvested energy to support uplink data transmission. This paper investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer (WPT). To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information (CSI) are considered, and their corresponding EE performance is analyzed. Under quality-of-service (QoS) requirements, an EE maximization problem is formulated, which is non-trivial due to non-convexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex, and then, develop a distributed alternating direction method of multipliers (ADMM)- based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.
Coining goldMEDAL: A New Contribution to Data Lake Generic Metadata Modeling
Venue: arXiv cs.DB 2021
Year: 2021

The rise of big data has revolutionized data exploitation practices and led to the emergence of new concepts. Among them, data lakes have emerged as large heterogeneous data repositories that can be analyzed by various methods. An efficient data lake requires a metadata system that addresses the many problems arising when dealing with big data. In consequence, the study of data lake metadata models is currently an active research topic and many proposals have been made in this regard. However, existing metadata models are either tailored for a specific use case or insufficiently generic to manage different types of data lakes, including our previous model MEDAL. In this paper, we generalize MEDAL's concepts in a new metadata model called goldMEDAL. Moreover, we compare goldMEDAL with the most recent state-of-the-art metadata models aiming at genericity and show that we can reproduce these metadata models with goldMEDAL's concepts. As a proof of concept, we also illustrate that goldMEDAL allows the design of various data lakes by presenting three different use cases.
On the Uniform Distribution of Regular Expressions
Venue: arXiv cs.FL 2021
Year: 2021

Although regular expressions do not correspond univocally to regular languages, it is still worthwhile to study their properties and algorithms. For the average case analysis one often relies on the uniform random generation using a specific grammar for regular expressions, that can represent regular languages with more or less redundancy. Generators that are uniform on the set of expressions are not necessarily uniform on the set of regular languages. Nevertheless, it is not straightforward that asymptotic estimates obtained by considering the whole set of regular expressions are different from those obtained using a more refined set that avoids some large class of equivalent expressions. In this paper we study a set of expressions that avoid a given absorbing pattern. It is shown that, although this set is significantly smaller than the standard one, the asymptotic average estimates for the size of the Glushkov automaton for these expressions does not differ from the standard case.
iMHS: An Incremental Multi-Hypothesis Smoother
Venue: arXiv cs.RO 2021
Year: 2021

State estimation of multi-modal hybrid systems is an important problem with many applications in the field robotics. However, incorporating discrete modes in the estimation process is hampered by a potentially combinatorial growth in computation. In this paper we present a novel incremental multi-hypothesis smoother based on eliminating a hybrid factor graph into a multi-hypothesis Bayes tree, which represents possible discrete state sequence hypotheses. Following iSAM, we enable incremental inference by conditioning the past on the future but we add to that the capability of maintaining multiple discrete mode histories, exploiting the temporal structure of the problem to obtain a simplified representation that unifies the multiple hypothesis tree with the Bayes tree. In the results section we demonstrate the generality of the algorithm with examples in three problem domains: lane change detection (1D), aircraft maneuver detection (2D), and contact detection in legged robots (3D).
Cross-layer based intermittent jamming schemes for securing energy-constraint networks
Venue: arXiv cs.IT 2021
Year: 2021

The Internet-of-Things (IoT) emerges as a paradigm to achieve ubiquitous connectivity via wireless communications between kinds of physical objects. Due to the wireless broadcasting nature and the energy constraint of physical objects, concerns on IoT security have triggered research on cooperative jamming based physical layer security. With the help of a cooperative jammer, existing solutions can effectively fight against eavesdroppers. However, these schemes are of high energy cost due to continuously transmitting jamming signals. To reduce the energy consumption, we propose a new idea of intermittent jamming and design five specific intermittent jamming schemes (IJSs). By taking the transmit frame formate into account, we optimize these IJSs from three aspects, including the jamming power, the jamming method, and the jamming positions. Then we analyze the applicability of the proposed IJSs according to different requirements on the synchronization, the available jamming energy and the jamming power constraints. Extensive MATLAB experiments are conducted on the basis of the WLAN Toolbox, which demonstrate the proposed IJSs can effectively degrade the reception of the eavesdropper and outperform the widespread continuous jamming scheme (CJS) when the available jamming energy is limited.
Proximally Optimal Predictive Control Algorithm for Path Tracking of Self-Driving Cars
Venue: arXiv cs.RO 2021
Year: 2021

This work presents proximally optimal predictive control algorithm, which is essentially a model-based lateral controller for steered autonomous vehicles that selects an optimal steering command within the neighborhood of previous steering angle based on the predicted vehicle location. The proposed algorithm was formulated with an aim of overcoming the limitations associated with the existing control laws for autonomous steering - namely PID, Pure-Pursuit and Stanley controllers. Particularly, our approach was aimed at bridging the gap between tracking efficiency and computational cost, thereby ensuring effective path tracking in real-time. The effectiveness of our approach was investigated through a series of dynamic simulation experiments pertaining to autonomous path tracking, employing an adaptive control law for longitudinal motion control of the vehicle. We measured the latency of the proposed algorithm in order to comment on its real-time factor and validated our approach by comparing it against the established control laws in terms of both crosstrack and heading errors recorded throughout the respective path tracking simulations.